EEG Based Sleep-Awake Classification Using Sample Entropy and Band Power Ratio

Gangadharan K. Sagila; A. P. Vinod

doi:10.1109/TENCON.2019.8929259

EEG Based Sleep-Awake Classification Using Sample Entropy and Band Power Ratio

Gangadharan K. Sagila, A. P. Vinod

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

2 Citations (Scopus)

Abstract

Detection and classification of sleep and awake of an individual has potential applications in biomedical engineering, high-risk work places, vigilance monitoring in Advanced Driver Assistance Systems, etc. In this paper we present a method to classify sleep and awake states using Electroencephalogram (EEG) signal. The proposed method makes use of sample entropy measure and band power ratio of EEG signal as suitable features for efficient classification. The classification is performed using Support Vector Machine (SVM) and an average classification accuracy of 96.28% is obtained on performing the classification among 30 subjects.

Original language	English
Title of host publication	Proceedings of the TENCON 2019
Subtitle of host publication	Technology, Knowledge, and Society
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2300-2304
Number of pages	5
ISBN (Electronic)	9781728118956
DOIs	https://doi.org/10.1109/TENCON.2019.8929259
Publication status	Published - Oct 2019
Externally published	Yes
Event	2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019 - Kerala, India Duration: 17 Oct 2019 → 20 Oct 2019

Publication series

Name	IEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume	2019-October
ISSN (Print)	2159-3442
ISSN (Electronic)	2159-3450

Conference

Conference	2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019
Country/Territory	India
City	Kerala
Period	17/10/19 → 20/10/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

band power
EEG
sample entropy
Sleep-awake

Access to Document

10.1109/TENCON.2019.8929259

Cite this

Sagila, G. K., & Vinod, A. P. (2019). EEG Based Sleep-Awake Classification Using Sample Entropy and Band Power Ratio. In Proceedings of the TENCON 2019: Technology, Knowledge, and Society (pp. 2300-2304). Article 8929259 (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2019-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TENCON.2019.8929259

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title = "EEG Based Sleep-Awake Classification Using Sample Entropy and Band Power Ratio",

abstract = "Detection and classification of sleep and awake of an individual has potential applications in biomedical engineering, high-risk work places, vigilance monitoring in Advanced Driver Assistance Systems, etc. In this paper we present a method to classify sleep and awake states using Electroencephalogram (EEG) signal. The proposed method makes use of sample entropy measure and band power ratio of EEG signal as suitable features for efficient classification. The classification is performed using Support Vector Machine (SVM) and an average classification accuracy of 96.28\% is obtained on performing the classification among 30 subjects.",

keywords = "band power, EEG, sample entropy, Sleep-awake",

author = "Sagila, \{Gangadharan K.\} and Vinod, \{A. P.\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019 ; Conference date: 17-10-2019 Through 20-10-2019",

year = "2019",

month = oct,

doi = "10.1109/TENCON.2019.8929259",

language = "English",

series = "IEEE Region 10 Annual International Conference, Proceedings/TENCON",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2300--2304",

booktitle = "Proceedings of the TENCON 2019",

}

Sagila, GK & Vinod, AP 2019, EEG Based Sleep-Awake Classification Using Sample Entropy and Band Power Ratio. in Proceedings of the TENCON 2019: Technology, Knowledge, and Society., 8929259, IEEE Region 10 Annual International Conference, Proceedings/TENCON, vol. 2019-October, Institute of Electrical and Electronics Engineers Inc., pp. 2300-2304, 2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019, Kerala, India, 17/10/19. https://doi.org/10.1109/TENCON.2019.8929259

EEG Based Sleep-Awake Classification Using Sample Entropy and Band Power Ratio. / Sagila, Gangadharan K.; Vinod, A. P.
Proceedings of the TENCON 2019: Technology, Knowledge, and Society. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2300-2304 8929259 (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2019-October).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

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AU - Vinod, A. P.

PY - 2019/10

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N2 - Detection and classification of sleep and awake of an individual has potential applications in biomedical engineering, high-risk work places, vigilance monitoring in Advanced Driver Assistance Systems, etc. In this paper we present a method to classify sleep and awake states using Electroencephalogram (EEG) signal. The proposed method makes use of sample entropy measure and band power ratio of EEG signal as suitable features for efficient classification. The classification is performed using Support Vector Machine (SVM) and an average classification accuracy of 96.28% is obtained on performing the classification among 30 subjects.

AB - Detection and classification of sleep and awake of an individual has potential applications in biomedical engineering, high-risk work places, vigilance monitoring in Advanced Driver Assistance Systems, etc. In this paper we present a method to classify sleep and awake states using Electroencephalogram (EEG) signal. The proposed method makes use of sample entropy measure and band power ratio of EEG signal as suitable features for efficient classification. The classification is performed using Support Vector Machine (SVM) and an average classification accuracy of 96.28% is obtained on performing the classification among 30 subjects.

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KW - EEG

KW - sample entropy

KW - Sleep-awake

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DO - 10.1109/TENCON.2019.8929259

M3 - Conference Paper published in a book

AN - SCOPUS:85077713912

T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON

SP - 2300

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BT - Proceedings of the TENCON 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019

Y2 - 17 October 2019 through 20 October 2019

ER -

EEG Based Sleep-Awake Classification Using Sample Entropy and Band Power Ratio

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Keywords

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